An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery
|
|
- Ethan Johnson
- 5 years ago
- Views:
Transcription
1 264 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 2, FEBRUARY 2001 An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery Shunlin Liang, Member, IEEE Abstract Land surface temperature (LST) and emissivity are important components of land surface modeling and applications. The only practical means of obtaining LST at spatial and temporal resolutions appropriate for most modeling applications is through remote sensing. While the popular split-window method has been widely used to estimate LST, it requires known emissivity values. Multispectral thermal infrared imagery provides us with an excellent opportunity to estimate both LST and emissivity simultaneously, but the difficulty is that a single multispectral thermal measurement with bands presents equations in + 1 unknowns ( spectral emissivities and LST). In this study, we developed a general algorithm that can separate land surface emissivity and LST from any multispectral thermal imagery, such as moderate-resolution imaging spectroradiometer (MODIS) and advanced spaceborne thermal emission and reflection radiometer (ASTER). The central idea was to establish empirical constraints, and regularization methods were used to estimate both emissivity and LST through an optimization algorithm. It allows us to incorporate any prior knowledge in a formal way. The numerical experiments showed that this algorithm is very effective (more than 43.4% inversion results differed from the actual LST within 0.5, 70.2% within 1 and 84% within 1.5 ), although improvements are still needed. Index Terms Emissivity, land surface temperature, remote sensing, thermal infrared. I. INTRODUCTION LAND surface temperature (LST) is required for a variety of climatic, hydrologic, ecological, and biogeochemical studies [1]. Emissivities are strongly indicative of composition, especially for the silicate minerals that make up much of the land surface. They are also important for bedrock mapping and resource exploration [2]. Both are needed in the accurate calculation of outgoing longwave radiation emitted from the Earth s surface [3]. There have been many algorithms proposed and implemented for the retrieval of LST from remote observations. In particular, much research has focused on methods that use two thermal channels of the advanced very high resolution radiometer (AVHRR) sensor [4] [6]. One common algorithm is the so-called split-window method, which has been used successfully for sea surface temperature retrievals [7], [8]. However, temperature derivation over land is more difficult than over the ocean because of the high spatial and temporal Manuscript received December 6, 1999; revised March 29, The author is with the Laboratory for Global Remote Sensing Studies, Department of Geography, University of Maryland, College Park, MD USA ( sliang@geog.umd.edu). Publisher Item Identifier S (01) variability of surface emissivity and atmospheric water vapor, both of which significantly affect the thermal radiance reaching the sensor (hence the recovered temperatures). The central part of most split-window algorithms is based on the assumption that LST is linearly related to the brightness temperatures of two thermal channels. With the assumption that surface emissivities of these two channels are known, the split-window method can eliminate atmospheric effects for LST estimation. More studies have revealed that when the atmosphere is not particularly dry, the traditional split-window algorithm cannot remove the atmospheric effects completely. Many efforts (e.g., [9], [10]) therefore have been made to incorporate the column water vapor content of the atmosphere into the split-window formulae. Estimating land surface emissivity at the global scale for being used in the split-window algorithms is very challenging. Several methods have been reported in the literature. One approach is to link emissivity with normalized difference vegetation index (NDVI) [11], [12]. Another approach is to associate it with land cover information [13] in which each cover type is assigned one emissivity value. Obviously, these two approaches cannot completely capture the tremendous amount of variability of surface emissivity, particularly over nonvegetated regions. An inaccuracy of only 0.01 in emissivity causes errors in LST exceeding those due to atmospheric correction [5]: In fact the error due to an error in the emissivity correction is two times larger than that due to an error in the atmospheric correction Certainly this is an area of research that requires much more study [14]. A very practical way for accurately estimating spectral emissivity is from thermal infrared imagery itself, which certainly requires multiple thermal channels. Fortunately, several spaceborne sensors will have multiple thermal infrared bands that enable us to estimate LST and emissivity simultaneously, such as MODIS and ASTER. The MODIS [15] has multiple thermal bands in the m and the m ranges, and the ASTER [16] has five thermal bands in the 8 12 m range (see Table I). The split-window algorithms combine atmospheric correction and the LST estimation into one process. However, it is necessary to correct the atmospheric effects before separating temperature and emissivity from multiple thermal infrared imagery. The ASTER science team has proposed to correct the atmospheric effects using the atmospheric profiles produced by the MODIS sounders [17]. The MODIS science team proposed a day/night algorithm [1] that adjusts the MODIS atmospheric /01$ IEEE
2 LIANG: OPTIMIZATION ALGORITHM 265 TABLE I profiles for each pixel with the surface air temperature and a scaling factor of the total water vapor amount as two unknown variables by solving a 14-equation set. Assuming that the atmospheric impacts can be effectively removed, we can therefore focus on estimating both LST and emissivity from multispectral thermal infrared imagery in this study. Both MODIS and ASTER teams have developed new algorithms for separating LST and emissivity from multiple thermal bands [1], [2], which are quite different in nature. Effects of LST and emissivity on thermal radiance are so closely coupled that their separation from thermal radiance measurements alone is quite difficult. This is because a single multispectral thermal measurement with bands presents equations in 1 unknowns ( spectral emissivities and LST). It is a typical ill-posed inversion problem. Without any prior information, it is impossible for us to recover both LST and emissivity exactly. Most LST-emissivity separation studies used one additional empirical equation so that measurements plus this additional equation can be solved for 1 unknowns. For example, the Alpha-derived emissivity (ADE) method [18], [19] makes use of the relation between the weighted logarithm values of spectral emissivity and the variance of spectral emissivities. The reference channel method [20] assumes that the value of the emissivity for one of the image channels is constant and known a priori, reducing the number of unknowns to the number of equations. The comparisons among different LST/emissivity separation algorithms have been well discussed by Gillespie et al. [2], [21] and Li et al. [22]. Based on previous algorithms, the ASTER team developed a new empirical equation, which was derived by using 86 samples. When more samples were available, the coefficients of this equation varied [2]. Based on nearly 1000 samples from different sources, we found that none of the empirical relationships in the previous studies hold. Fig. 1 shows the relationship between the minimum emissivity and the range of emissivities. There is a tremendous amount of variability in this figure, which may result in a possible failure of such an empirical equation. This demonstrates a need for developing new algorithms. In this study, we developed a new empirical equation. Moreover, our algorithm utilizes an optimal inversion algorithm that has solid foundations in computational mathematics and different regularization techniques for stabilizing the final solutions. In this context, we can easily incorporate any possible prior information and knowledge into this iterative procedure. II. NEW APPROACH The central idea of the proposed algorithm is very similar to the previous algorithms in the sense that we created a new equation so that 1 equations are used to solve 1 unknown variables. However, since any added equations are based on empirical formulae, the solutions are likely to be unstable. It is a typical ill-posed inversion problem. According to Moritz [23], the inversion problem is called properly posed if the solution satisfies the following requirements: 1) the solution must exist (existence); 2) the solution must be uniquely determined by the data (uniqueness); and 3) the solution must depend continuously on the data (stability). If one or more these requirements are violated, then we have an improperly posed, orill-posed problem. In our present case, it is an ill-posed problem since observations cannot determine 1 unknowns uniquely. Additional information is needed to turn it to a properly posed problem. One way of providing this additional information is the use of appropriate constraints. This process is known as regularization of the ill-posed problem. Different regularization methods [24], [25] were explored to stabilize the solutions in this study. Nearly 1000 surface emissivity spectra of different cover types provided by Dr. Salisbury and from the JPL spectral library were integrated to the ASTER and MODIS bands using their sensor spectral response functions. We developed a new empirical relation to predict the minimum emissivity for the MODIS and ASTER, respectively. This inversion algorithm is quite general and suitable for any multispectral thermal infrared remote sensing. We simply take MODIS and ASTER as examples while discussing this new inversion algorithm below, but this method has general applicability. A. MODIS The MODIS sensor has a total of 36 bands and more than a dozen thermal bands. The MODIS science team has proposed to make use of seven bands for LST and emissivity estimation [1]. Based on our MODTRAN simulations, we found that band 33 has very low atmospheric transmittance (lower than 0.35 in many cases) under different atmospheric conditions. We therefore consider only six bands in this study. Their spectral wavelengths are shown in Table I. The spatial resolution of MODIS thermal bands is 1 km, and the scanning angle is as large as 55. It is carried by Terra (EOS-AM platform) and acquires data at 10:30am local time and will be also carried by the EOS-PM platform acquiring data at 2:30pm local time. The general empirical equation for the MODIS implementation of our method is given by where and are the median and range of absolute emissivities of 6 bands. The fitted results were very good, with the R-squared value of and the residual standard error (RSE) of If the emissivity spectra is quite smooth, the uncertainty associated with (1) does not affect the inversion results significantly. However, it has been realized from the exploratory experiments that the separation of emissivity and LST is highly sensitive to this additional empirical relation. Based on the observations in Fig. 1(a), we established specific empirical (1)
3 266 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 2, FEBRUARY 2001 (a) (b) Fig. 1. Relations between the minimum emissivity and the range of the emissivity values for both (a) MODIS and (b) ASTER. A few outliers in the lower left corner were excluded. equations for several ranges of the maximum emissivity that had significantly improved the results. If (3) If (2) If (4)
4 LIANG: OPTIMIZATION ALGORITHM 267 Fig. 2. Fitted minimum emissivity again the actual MODIS minimum emissivity values using (2) (6). If If (5) The fitting result was similar to the MODIS analysis, with an R-squared value of and RSE of Likewise, a further improvement in temperature and emissivity estimates was made by fitting similar equations for several ranges of the maximum emissivity. If 0.85 (7) The fitting results were much better (Fig. 2), and the RSE decreases to Any other prior knowledge might help to determine the solutions more accurately. After examining these spectral emissivity spectra, we found many additional relations that can be used to constrain the LST and emissivity separation. These relations can be derived from these constraints, as illustrated in Fig. 3. B. ASTER The ASTER sensor has five thermal bands (see Table I) with the spatial resolution of 90 m, and the scanning angle is less than 8.5. It is also carried by Terra. The general empirical equation we developed for the ASTER estimates is given by (6) If If If (8) (9) (10) (11)
5 268 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 2, FEBRUARY 2001 Fig. 3. Illustration of the conditional constraints used for retrieving MODIS surface emissivities. The envelopes banded by two dashed lines indicate the variation ranges of these MODIS band emissivities. If (12) The results were significantly better (Fig. 4). Similar to the MODIS analysis, we also found many additional constraints based on the range of data values used in the estimations. Some of these constraints are illustrated in Fig. 5. C. Inversion Procedure To estimate 1 unknowns from 1 nonlinear equations, a multidimensional optimization algorithm is needed. For global applications, computational speed is a major concern. On the other hand, the added empirical equation does not fit the data perfectly. If we treat it as a perfect equation, the uncertainty is introduced into the inversion process. Instead, we developed a constrained one-dimensional (1-D) inversion procedure. The 1-D optimum inversion algorithm for estimating LST is to min-
6 LIANG: OPTIMIZATION ALGORITHM 269 Fig. 4. Fitted minimum emissivity again the actual ASTER minimum emissivity values using (8) (12). imize the merit function that consists of a sum of squares (SS) term and a penalty function (13) The SS term is expressed by the smoothness of the retrieved emissivity curve. Different regularization techniques produce different measures of the smoothness [25], [24]. Four regularization techniques were explored in this study power variance first-order difference second-order difference (14) where is the emissivity value at band, is the mean value of the spectral emissivity curve. This first measure corresponds to the power of the emissivity signal. The second technique is closely related to the first one, which eventually is the variance of the spectral emissivity curve. The third and forth technique is based on the first-order and second-order differences of the emissivity curve. The penalty function is designed to force all estimated parameters in reasonable ranges. If the value of a variable is beyond its range, a huge penalty is posed (with the huge weight 10 ). This huge weight was recommended by Siddall [26]. In our previous studies [27] [30], we found Siddall algorithm is very effective. In particularly, we can easily incorporate the new empirical equation and other constraints into the penalty term. Rather than requiring a perfect additional equation, we just force all points to be within the envelope around the regression line that defines the empirical equation. For MODIS, if (1) is used the envelope is defined by If (2) (6) are used, the envelope is defined by Similarly, for the ASTER, if (7) is used, for example, If (8) (12) are used, From these envelopes, we can see that the fitting formulae (2) (6) and (8) (12) can produce better results. Estimation of LST and emissivity values from (13) is an typical nonlinear optimization problem that determines the solutions iteratively. The iterative process works as follows. Given an initial LST ( ), band emissivities are derived based on (15) where and are the surface-leaving radiance and downward sky radiance from the atmospheric correction procedure, and is the blackbody radiance that can be calculated by integrating radiance using the Planck formula with the
7 270 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 2, FEBRUARY 2001 Fig. 5. Illustration of the conditional constraints used for retrieving ASTER surface emissivities. The envelopes banded by two dashed lines indicate the variation ranges of these ASTER band emissivities. sensor band spectral response functions. The Planck formula for radiance with the unit WATTS CM STER m is given by (16) where is the wavenumber (10 000/, is the wavelength in m). Note that this algorithm is based on the assumption that observed radiance has been corrected atmospherically. There is therefore no path radiance and transmittance in (15). After determining, and, is predicted based on (1) or (2) (6) for the MODIS, and (7) or (8) (12) for the ASTER. The merit function (13) is then calculated. For the next iteration, the iteration length and iteration direction must be found optimally. The iteration continues until the convergence reaches (i.e., smaller than a threshold). There are at least four major differences between the present new algorithm and the published algorithms in the literature: 1) the empirical equations are dramatically different; 2) regularization methods are applied; 3) more prior information have been defined and naturally incorporated into the new algorithm in a formal manner; and 4) the new algorithm uses an optimal inversion algorithm that has solid foundations in computational mathematics. III. NUMERICAL EXPERIMENTS To evaluate the performance of this new approach, we conducted a series of numerical inversion experiments. The numerical experiments serve as the first step for testing any new algorithms. Field experiments ultimately should validate the new approaches, but many conditions (e.g., and ) cannot be easily measured in the field. As a result, it is not easy to determine the model accuracy using measured data from the field. We arbitrarily selected 10% of the profiles (590 in total) from our atmospheric profile database, which is composed of profiles from the NAVY profile model, and the measured real profiles from the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), Boreal Ecosystems Atmosphere Study (BOREAS), and First ISLSCP Field Experiment (FIFE). LST was then assumed to be equal to the air temperature at the surface level, which varies from 268 to A total of 937 emissivity spectra of soil, rocks, minerals and vegetation and these atmospheric profiles were input to MODTRAN to simulate downward sky fluxes and surface leaving radiance. A
8 LIANG: OPTIMIZATION ALGORITHM 271 Fig. 6. Histogram of the actual and retrieved LST difference from the simulated MODIS data. total of observations for both MODIS and ASTER were generated by integrating the MODTRAN outputs with these sensor spectral response functions. We compared the performance of these four regularization techniques (14) that were discussed in the previous section. In most cases, the variance measure and the first order difference measure performed the best, and other two techniques performed less well. In the following, all reported results only came from the variance regularization technique using the simulated MODIS data. The objective of this numerical experiment was to determine if this new inversion algorithm can accurately retrieve the range of surface emissivities and temperature values. The results indicated that we could not inverse both LST and emissivities perfectly. The histogram of the difference between the retrieved LST and actual LST is shown in Fig. 6. However, more than 43.4% inversion results differed from the actual LST within 0.5, 70.2% within 1 and 84% within 1.5. The regression between the actual LST ( ) and the retrieved LST ( ) was a little biased: (17) The R-squared value was 0.98, and the residual standard error was 1.1. The retrieved emissivities of individual bands are displayed in Fig. 7. The residual standard errors ranged from at bands 31 and 32 to at band 20, the R-squared values varied from at band 32 to at band 22. Since LST was not perfectly retrieved, the retrieved emissivities also could not match the actual emissivities perfectly. The errors may come from different sources. The majority of the error is attributed to the empirical equation, which is only a best fit for a given emissivity range. Four cases are presented in Fig. 8 to illustrate this point. The solid lines stand for the actual emissivity curves. In case 1, two sets of solutions were found, and the retrieved LST was about 15 from the actual LST. Case 3 was similar, although the LST difference was much smaller. In cases 2 and 4, three sets of solutions were found and they all satisfied the empirical relations. If we examined these cases carefully, we found that the retrieved emissivity curves look very similar to the actual ones, although their magnitudes might be quite different. More importantly, all emissivity curves increase with wavelength similar to the blackbody emitted radiance, which explains why shifting emissivity curse can still make all 1 equations valid. It is found that one method can reduce the uncertainties to some extent. One can run the inversion code for times where is the number of thermal bands. The median value of the retrieved multiple solution is defined as the final solution. The initial values are determined in this way. Assuming unity emissivity, the initial value is solved from the Planck equation using the central wavelength of that band, which is calculated as where is the sensor spectral response function. Obviously, there are increased computational expenses.
9 272 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 2, FEBRUARY 2001 Fig. 7. Retrieved MODIS band emissivities and their actual surface emissivities. When the same inversion code was run over the simulated ASTER data, the results were very similar. No details are reported here. IV. SUMMARY In this study, we developed a general method for retrieving both LST and emissivity simultaneously from any multispectral thermal infrared imagery. An empirical equation based on the measured emissivity spectra was established. Four different regularization techniques were explored and an optimization algorithm was used to determine the solutions. One of the features of this method is its ability to incorporate any prior knowledge about the spectral emissivities and LST into the inversion algorithm. Numerical experiments were conducted to test this inversion algorithm. Near 1000 emissivity spectra were integrated into MODIS and ASTER thermal bands in conjunction with the spectral response functions of these sensors. Different atmospheric conditions using a range of LSTs were used to simulate the MODIS and ASTER surface leaving radiance and sky radiance. The optimization code was used to estimate LST and spectral emissivities simultaneously. The results showed that both LST and spectral emissivity can be estimated with a reasonably good accuracy (more than 70.2% inversion results differed from the actual LST within 1 ). Two regularization techniques (variance and first-order difference) worked very well, while other two regularization techniques (power and second order difference) worked less well in our experiments. From these numerical experiments, we found that if the emissivity values of all bands were very low, the solution was very likely to be unstable. In a few cases, the maximum LST difference could be more than 10. Further algorithm developments are needed to address this issue. This method works for surface-leaving radiance, an output from the atmospheric correction procedure. The accuracy of atmospheric correction of multispectral thermal infrared imagery will eventually affect the separation of LST and emissivity. The discussion of atmospheric correction is beyond this paper, but it is a critical issue that we need to pay more attention to in the future. To validate this algorithm using ground measurements, we are also making efforts to apply this algorithm to the airborne multispectral thermal data from the MODIS airborne simulator (MAS) and the airborne thermal infrared multispectral scanner (TIMS) sensors. The results will be reported later.
10 LIANG: OPTIMIZATION ALGORITHM 273 Fig. 8. Illustration of four cases that produce more than seven solutions with six MODIS observations and one additional equation. ACKNOWLEDGMENT The author would like to thank the MODTRAN Development Team, Dr. J. Salisbury for providing the measured emissivity spectra, and the ASTER Science Team for providing the ASTER Spectral Library data. He would also like to thank the anonymous reviewers for their valuable comments, which greatly improved the presentation of this paper. REFERENCES [1] Z. Wan, MODIS Land Surface Temperature Algorithm Theoretical Basis Documentation, Version 3.3, Apr [2] A. R. Gillespie, S. Rokugawa, T. Matsunaga, J. Cothern, S. Hook, and A. Kahle, A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, IEEE Trans. Geosci. Remote Sensing, vol. 36, pp , July [3] P. Rowntree, Atmospheric parameterization schemes for evaporation over land: Basic concepts and climate modeling aspects, in Land Surface Evaporation: Measurement and Parameterization, T. Schmugge and J. Abdre, Eds., 1991, pp [4] J. C. Price, Land surface temperature measurements from the split window channels of the NOAA-7/AVHRR, J. Geophys. Res., vol. 89, pp , [5] Z. M. Wan and J. Dozier, Land-surface temperature measurement from space: Physical principles and inverse modeling, IEEE Trans. Geosci. Remote Sensing, vol. 27, pp , Jan [6] A. J. Prata, Land surface temperatures derived from the advanced very high resolution ra diometer and the along-track scanning radiometer 1: Theory, J. Geophys. Res., vol. 98, pp , [7] E. P. McClain, W. G. Pichel, and C. C. Walton, Comparative performance of AVHRR-based multichannel sea surface temperatures, J. Geophys. Res., vol. 90, pp , [8] A. F. Pearce, A. J. Prata, and C. R. Manning, Comparison of NOAA/AVHRR-2 sea surface temperatures with surface measurements in coastal waters, Int. J. Remote Sensing, vol. 10, pp , [9] J. A. Sobrino, C. Coll, and V. Caselles, Atmospheric corrections for land surface temperature using AVHRR channel 4 and 5, Remote Sens. Environ., vol. 38, pp , [10] F. Becker and Z. Li, Surface temperature and emissivity at various scales: Definition, measurement and related problems, Remote Sens. Rev., vol. 12, pp , [11] A. A. Van De Griend and M. Owe, On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces, Int. J. Remote Sensing, vol. 14, pp , [12] E. Valor and V. Caselles, Mapping land surface emissivity from NDVI: Application to European, African, and South American areas, Remote Sens. Environ., pt. 1, vol. 57, pp , [13] W. Snyder, Z. Wan, Y. Zhang, and Y. Feng, Classification-based emissivity for land surface temperature measurement from space, Int. J. Remote Sensing, vol. 19, pp , [14] A. J. Prata, V. Caselles, C. Coll, J. Sobrino, and C. Ottle, Thermal remote sensing of land surface temperature from satellites: Current status and future prospects, Remote Sens. Rev., vol. 12, pp , [15] V. Salomonson, W. Barnes, P. Maymon, H. Montgomery, and H. Ostrow, MODIS: advanced facility instrument for studies of the Earth as a system, IEEE Trans. Geosci. Remote Sensing, vol. 27, pp , Jan [16] Y. Yamaguchi, A. Kahle, H. Tsu, T. Kawakami, and M. Pniel, Overview of advanced spaceborne thermal emission and reflection radiometer (ASTER), IEEE Trans. Geosci. Remote Sensing, vol. 36, pp , July [17] F. Palluconi, G. Hoover, R. Alley, M. Jentoft-Nilsen, and T. Thompson, An Atmospheric Correction Method for ASTER Thermal Radiometry over Land, Revision 2. Washington, DC: NASA, Aug [18] P. Kealy and S. Hook, Separating temperature and emissivity in thermal infrared multispectral scanner data: Implication for recovering land surface temperatures, IEEE Trans. Geosci. Remote Sensing, vol. 31, pp , Nov
11 274 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 2, FEBRUARY 2001 [19] S. Hook, A. Gabell, A. Green, and P. Kealy, A comparison of techniques for extracting emissivity information from thermal infrared data for geological studies, Remote Sens. Environ., vol. 42, pp , [20] R. J. P. Lyon, Analysis of rocks by spectral infrared emission (8 to 25 microns), Econ. Geol., vol. 60, pp , [21] A. R. Gillespie, S. Rokugawa, S. Hook, T. Matsunaga, and A. Kahle, ASTER Temperature/Emissivity Separation Algorithm Theoretical Basis Document, Version 2.4. Washington, DC: NASA, Mar [22] Z.-L. Li, F. Becker, M. Stoll, and Z. Wan, Evaluation of six methods for extracting relative emissivity spectra from thermal infrared images, Remote Sens. Environ., vol. 69, pp , [23] H. Moritz, General considerations regarding inverse and related problems, in Inverse Problems: Principles and Applications in Geophysics, Technology, and Medicine, G. Anger, R. Gorenflo, H. Jochmann, H. Moritz, and W. Webers, Eds. Berlin, Germany: Akademie Verlag, 1993, pp [24] S. Twomey, Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements. Mineola, NY: Dover, [25] A. Tikhonov and V. Arsenin, Solutions of Ill-Posed Problems. Washington, DC: V. H. Winston and Sons, [26] J. N. Siddall, Analytical Decision-making in Engineering Design. Englewood Cliffs, NJ: Prentice-Hall, [27] S. Liang and A. H. Strahler, An analytic canopy radiative transfer BRDF model and its inversion, IEEE Trans. Geosci. Remote Sensing, vol. 31, pp , Sept [28] S. Liang and J. R. G. Townshend, Parametric soil BRDF model: A fourstream approximation, Int. J. Remote Sensing, vol. 17, pp , [29], A modified Hapke model for soil bidirectional reflectance, Remote Sens. Environ., vol. 55, pp. 1 10, [30] S. Liang and A. H. Strahler, Retrieval of surface BRDF parameters from multiangle remotely sensed data, Remote Sens. Environ., vol. 50, pp , Shunlin Liang (M 94) received the Ph.D. degree in remote sensing and GIS from Boston University, Boston, MA, in He was a Postdoctoral Research Associate with Boston University from 1992 to 1993, and Validation Scientist of the NOAA/NASA Pathfinder AVHRR Land Project from 1993 to He is currently an Assistant Professor with the University of Maryland, College Park. His present research interests focus on land surface data assimilation, parameter retrieval from remotely sensed data, and spatial analysis. He is the Principal Investigator of the NASA EOS Terra Validation Team, the NASA Earth Observer-1 Science Team, and the International ALOS, CHRIS/PROBA, and POLDER Science Working Teams. He organized the International Forum on BRDF in San Francisco, CA, in December 1998, and edited a special issue of Remote Sensing Reviews. Dr. Liang chaired various sessions in the international conferences and serves as chairman of IEEE Geosciences and Remote Sensing Society, Washington/North Virginia Chapter, 2000.
THE ADVANCED Spaceborne Thermal Emission and
60 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 4, NO. 1, JANUARY 2007 Feasibility of Retrieving Land-Surface Temperature From ASTER TIR Bands Using Two-Channel Algorithms: A Case Study of Agricultural
More informationEstimation of broadband emissivity (8-12um) from ASTER data by using RM-NN
Estimation of broadband emissivity (8-12um) from ASTER data by using RM-NN K. B. Mao, 1,2, 7 Y. Ma, 3, 8 X. Y. Shen, 4 B. P. Li, 5 C. Y. Li, 2 and Z. L. Li 6 1 Key Laboratory of Agri-informatics, MOA,
More informationMONITORING THE SURFACE HEAT ISLAND (SHI) EFFECTS OF INDUSTRIAL ENTERPRISES
MONITORING THE SURFACE HEAT ISLAND (SHI) EFFECTS OF INDUSTRIAL ENTERPRISES A. Şekertekin a, *, Ş. H. Kutoglu a, S. Kaya b, A. M. Marangoz a a BEU, Engineering Faculty, Geomatics Engineering Department
More informationMapping Surface Broadband Emissivity of the Sahara Desert Using ASTER and MODIS Data
Earth Interactions Volume 8 (2004) Paper No. 7 Page 1 Copyright Ó 2004, Paper 8-007; 4,089 words, 7 Figures, 0 Animations, 3 Tables. http://earthinteractions.org Mapping Surface Broadband Emissivity of
More informationSimulation and validation of land surface temperature algorithms for MODIS and AATSR data
Tethys, 4, 27 32, 2007 www.tethys.cat ISSN-1697-1523 eissn-1139-3394 DOI:10.3369/tethys.2007.4.04 Journal edited by ACAM (Associació Catalana de Meteorologia) Simulation and validation of land surface
More informationAnalysing Land Surface Emissivity with Multispectral Thermal Infrared Data
2nd Workshop on Remote Sensing and Modeling of Surface Properties 9-11 June 2009, Météo France, Toulouse, France Analysing Land Surface Emissivity with Multispectral Thermal Infrared Data Maria Mira, Thomas
More informationDiscrimination of Senescent Vegetation Using Thermal Emissivity Contrast
Discrimination of Senescent Vegetation Using Thermal Emissivity Contrast A. N. French,* T. J. Schmugge,* and W. P. Kustas* A remote sensing method utilizing multiband thermal ble to determine in detail
More informationGlobal Broadband IR Surface Emissivity Computed from Combined ASTER and MODIS Emissivity over Land (CAMEL)
P76 Global Broadband IR Surface Emissivity Computed from Combined ASTER and MODIS Emissivity over Land (CAMEL) Michelle Feltz, Eva Borbas, Robert Knuteson, Glynn Hulley*, Simon Hook* University of Wisconsin-Madison
More informationDefining microclimates on Long Island using interannual surface temperature records from satellite imagery
Defining microclimates on Long Island using interannual surface temperature records from satellite imagery Deanne Rogers*, Katherine Schwarting, and Gilbert Hanson Dept. of Geosciences, Stony Brook University,
More informationAGRICULTURE DROUGHT AND FOREST FIRE MONITORING IN CHONGQING CITY WITH MODIS AND METEOROLOGICAL OBSERVATIONS *
AGRICULTURE DROUGHT AND FOREST FIRE MONITORING IN CHONGQING CITY WITH MODIS AND METEOROLOGICAL OBSERVATIONS * HONGRUI ZHAO a ZHONGSHI TANG a, BIN YANG b AND MING ZHAO a a 3S Centre, Tsinghua University,
More informationESTIMATION OF ATMOSPHERIC COLUMN AND NEAR SURFACE WATER VAPOR CONTENT USING THE RADIANCE VALUES OF MODIS
ESTIMATION OF ATMOSPHERIC COLUMN AND NEAR SURFACE WATER VAPOR CONTENT USIN THE RADIANCE VALUES OF MODIS M. Moradizadeh a,, M. Momeni b, M.R. Saradjian a a Remote Sensing Division, Centre of Excellence
More informationLand Surface Temperature Measurements From the Split Window Channels of the NOAA 7 Advanced Very High Resolution Radiometer John C.
Land Surface Temperature Measurements From the Split Window Channels of the NOAA 7 Advanced Very High Resolution Radiometer John C. Price Published in the Journal of Geophysical Research, 1984 Presented
More informationLand Surface Temperature Retrieval from MODIS Data by Integrating Regression Models and the Genetic Algorithm in an Arid Region
Remote Sens. 2014, 6, 5344-5367; doi:10.3390/rs6065344 Article OPEN ACCESS remote sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Land Surface Temperature Retrieval from MODIS Data by Integrating
More informationUSE OF SATELLITE REMOTE SENSING IN HYDROLOGICAL PREDICTIONS IN UNGAGED BASINS
USE OF SATELLITE REMOTE SENSING IN HYDROLOGICAL PREDICTIONS IN UNGAGED BASINS Venkat Lakshmi, PhD, P.E. Department of Geological Sciences, University of South Carolina, Columbia SC 29208 (803)-777-3552;
More informationAATSR derived Land Surface Temperature from heterogeneous areas.
AATSR derived Land Surface Temperature from heterogeneous areas. Guillem Sòria, José A. Sobrino Global Change Unit, Department of Thermodynamics, Faculty of Physics, University of Valencia, Av Dr. Moliner,
More informationAssimilating terrestrial remote sensing data into carbon models: Some issues
University of Oklahoma Oct. 22-24, 2007 Assimilating terrestrial remote sensing data into carbon models: Some issues Shunlin Liang Department of Geography University of Maryland at College Park, USA Sliang@geog.umd.edu,
More informationExtraction of incident irradiance from LWIR hyperspectral imagery
DRDC-RDDC-215-P14 Extraction of incident irradiance from LWIR hyperspectral imagery Pierre Lahaie, DRDC Valcartier 2459 De la Bravoure Road, Quebec, Qc, Canada ABSTRACT The atmospheric correction of thermal
More informationVIIRS narrowband to broadband land surface albedo conversion: formula and validation
International Journal of Remote Sensing Vol. 26, No. 5, 10 March 2005, 1019 1025 VIIRS narrowband to broadband land surface albedo conversion: formula and validation S. LIANG*{, Y. YU{ and T. P. DEFELICE{
More informationAn Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations
15 JUNE 2006 J I N A N D L I A N G 2867 An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations MENGLIN JIN Department of Atmospheric and Oceanic
More informationTemperature and Emissivity from AHS data in the framework of the AGRISAR and EAGLE campaigns
AGRISAR and EAGLE Campaigns Final Workshop 15-16 October 2007 (ESA/ESTEC, Noordwijk, The Netherlands) Temperature and Emissivity from AHS data in the framework of the AGRISAR and EAGLE campaigns J. A.
More informationSAIL thermique, a model to simulate land surface emissivity (LSE) spectra
SAIL thermique, a model to simulate land surface emissivity (LSE) spectra Albert Olioso, INRA, UMR EMMAH (INRA UAPV), Avignon, France Frédéric Jacob, Audrey Lesaignoux IRD, UMR LISAH, Montpellier, France
More informationAATSR DERIVED LAND SURFACE TEMPERATURE OVER A HETEROGENEOUS REGION
AATSR DERIVED LAND SURFACE TEMPERATURE OVER A HETEROGENEOUS REGION José A. Sobrino (1), Guillem Sòria (1), Juan C. Jiménez- Muñoz (1), Belen Franch (1), Victoria Hidalgo (1) and Elizabeth Noyes (2). (1)
More informationA simple model for estimation of snow/ice surface temperature of Antarctic ice sheet using remotely sensed thermal band data
Indian Journal of Radio & Space Physics Vol 44, March 2015, pp 51-55 A simple model for estimation of snow/ice surface temperature of Antarctic ice sheet using remotely sensed thermal band data H S Gusain
More informationP1.20 MICROWAVE LAND EMISSIVITY OVER COMPLEX TERRAIN: APPLIED TO TEMPERATURE PROFILING WITH NOGAPS ABSTRACT
P1.0 MICROWAVE LAND EMISSIVITY OVER COMPLEX TERRAIN: APPLIED TO TEMPERATURE PROFILING WITH NOGAPS Benjamin Ruston *1, Thomas Vonder Haar 1, Andrew Jones 1, and Nancy Baker 1 Cooperative Institute for Research
More informationAPPLICATIONS WITH METEOROLOGICAL SATELLITES. W. Paul Menzel. Office of Research and Applications NOAA/NESDIS University of Wisconsin Madison, WI
APPLICATIONS WITH METEOROLOGICAL SATELLITES by W. Paul Menzel Office of Research and Applications NOAA/NESDIS University of Wisconsin Madison, WI July 2004 Unpublished Work Copyright Pending TABLE OF CONTENTS
More informationRevisions to the ASTER temperature/emissivity separation algorithm
Revisions to the ASTER temperature/emissivity separation algorithm William T. Gustafson, Alan R. Gillespie, and Gail J. Yamada Department of Earth and Space Sciences University of Washington Seattle WA
More informationAn Algorithm for Retrieving Land Surface Temperatures Using VIIRS Data in Combination with Multi-Sensors
Sensors 2014, 14, 21385-21408; doi:10.3390/s141121385 Article OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journal/sensors An Algorithm for Retrieving Land Surface Temperatures Using VIIRS Data in Combination
More informationsensors ISSN
Sensors 2009, 9, 1054-1066; doi:10.3390/s90201054 Article OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journal/sensors Discrepancy Between ASTER- and MODIS- Derived Land Surface Temperatures: Terrain
More informationSensitivity Analysis on Sea Surface Temperature Estimation Methods with Thermal Infrared Radiometer Data through Simulations
Sensitivity Analysis on Sea Surface Temperature Estimation Methods with Thermal Infrared Radiometer Data through Simulations Kohei Arai 1 Graduate School of Science and Engineering Saga University Saga
More informationFall Semester 2010 George Mason University Fairfax, VA
Research on Deriving Consistent Land Surface Temperature from the Geostationary Operational Environmental Satellite Series A thesis submitted in partial fulfillment of the requirements for the degree of
More informationENERGY IN THE URBAN ENVIRONMENT: USE OF TERRA/ASTER IMAGERY AS A TOOL IN URBAN PLANNING N. CHRYSOULAKIS*
ENERGY IN THE URBAN ENVIRONMENT: USE OF TERRA/ASTER IMAGERY AS A TOOL IN URBAN PLANNING N. CHRYSOULAKIS* Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics,
More informationSensitivity Study of the MODIS Cloud Top Property
Sensitivity Study of the MODIS Cloud Top Property Algorithm to CO 2 Spectral Response Functions Hong Zhang a*, Richard Frey a and Paul Menzel b a Cooperative Institute for Meteorological Satellite Studies,
More informationA Quantitative and Comparative Analysis of Linear and Nonlinear Spectral Mixture Models Using Radial Basis Function Neural Networks
2314 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 8, AUGUST 2001 Pasadena, CA for the use of their data. They would also like to acknowledge the useful comments by one anonymous reviewer.
More informationComparison of NASA AIRS and MODIS Land Surface Temperature and Infrared Emissivity Measurements from the EOS AQUA platform
Comparison of NASA AIRS and MODIS Land Surface Temperature and Infrared Emissivity Measurements from the EOS AQUA platform Robert Knuteson, Steve Ackerman, Hank Revercomb, Dave Tobin University of Wisconsin-Madison
More informationP2.7 A GLOBAL INFRARED LAND SURFACE EMISSIVITY DATABASE AND ITS VALIDATION
P2.7 A GLOBAL INFRARED LAND SURFACE EMISSIVITY DATABASE AND ITS VALIDATION Eva E. Borbas*, Leslie Moy, Suzanne W. Seemann, Robert O. Knuteson, Paolo Antonelli, Jun Li, Hung-Lung Huang, Space Science and
More informationLAND SURFACE TEMPERATURE VALIDATION WITH IN SITU MEASUREMENTS
LAND SURFACE TEMPERATURE VALIDATION WITH IN SITU MEASUREMENTS Group 7 Juan Manuel González Cantero Irene Grimaret Rincón Alex Webb Advisor: Darren Ghent Research problem The project task is to design an
More informationAn Automatic Instrument to Study the Spatial Scaling Behavior of Emissivity
Sensors 2008, 8, 800-816 sensors ISSN 1424-8220 2008 by MDPI www.mdpi.org/sensors Full Research Paper An Automatic Instrument to Study the Spatial Scaling Behavior of Emissivity Jing Tian 1, Renhua Zhang
More informationEvaluation of Regressive Analysis Based Sea Surface Temperature Estimation Accuracy with NCEP/GDAS Data
Evaluation of Regressive Analysis Based Sea Surface Temperature Estimation Accuracy with NCEP/GDAS Data Kohei Arai 1 Graduate School of Science and Engineering Saga University Saga City, Japan Abstract
More informationEstimating Temperature and Emissivity from the DAIS Instrument
Estimating Temperature and Emissivity from the DAIS Instrument C. Coll 1, V. Caselles 1, E. Rubio 1, E. Valor 1, F. Sospedra 1, F. Baret 2 and. Prévot 2 1 Department of Thermodynamics, Faculty of Physics,
More informationEstimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies
GEOPHYSICAL RESEARCH LETTERS, VOL. 32, L13606, doi:10.1029/2005gl022917, 2005 Estimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies
More informationA high spectral resolution global land surface infrared emissivity database
A high spectral resolution global land surface infrared emissivity database Eva E. Borbas, Robert O. Knuteson, Suzanne W. Seemann, Elisabeth Weisz, Leslie Moy, and Hung-Lung Huang Space Science and Engineering
More informationPrincipal Component Analysis (PCA) of AIRS Data
Principal Component Analysis (PCA) of AIRS Data Mitchell D. Goldberg 1, Lihang Zhou 2, Walter Wolf 2 and Chris Barnet 1 NOAA/NESDIS/Office of Research and Applications, Camp Springs, MD 1 QSS Group Inc.
More informationSatellite-based Lake Surface Temperature (LST) Homa Kheyrollah Pour Claude Duguay
Satellite-based Lake Surface Temperature (LST) Homa Kheyrollah Pour Claude Duguay Lakes in NWP models Interaction of the atmosphere and underlying layer is the most important issue in climate modeling
More informationSpectral surface emissivity for use in assimilation of IR radiance data over land
Spectral surface emissivity for use in assimilation of IR radiance data over land 1 2 Małgorzata Szczech-Gajewska, Florence Rabier 1 Institute of Meteorology and Water Management ul. P. Borowego 14, Kraków,
More informationImpact of NASA EOS data on the scientific literature: 16 years of published research results from Terra, Aqua, Aura, and Aquarius
Impact of NASA EOS data on the scientific literature: 16 years of published research results from Terra, Aqua, Aura, and Aquarius Gene R. Major NASA Goddard Library Nebulous Connections April 4, 2017 RESACs/RA
More informationHyperspectral Atmospheric Correction
Hyperspectral Atmospheric Correction Bo-Cai Gao June 2015 Remote Sensing Division Naval Research Laboratory, Washington, DC USA BACKGROUND The concept of imaging spectroscopy, or hyperspectral imaging,
More informationAdvantageous GOES IR results for ash mapping at high latitudes: Cleveland eruptions 2001
GEOPHYSICAL RESEARCH LETTERS, VOL. 32, L02305, doi:10.1029/2004gl021651, 2005 Advantageous GOES IR results for ash mapping at high latitudes: Cleveland eruptions 2001 Yingxin Gu, 1 William I. Rose, 1 David
More informationFractional Vegetation Cover Estimation from PROBA/CHRIS Data: Methods, Analysis of Angular Effects and Application to the Land Surface Emissivity
Fractional Vegetation Cover Estimation from PROBA/CHRIS Data: Methods, Analysis of Angular Effects and Application to the Land Surface Emissivity J.C. Jiménez-Muñoz 1, J.A. Sobrino 1, L. Guanter 2, J.
More informationHYPERSPECTRAL IMAGING
1 HYPERSPECTRAL IMAGING Lecture 9 Multispectral Vs. Hyperspectral 2 The term hyperspectral usually refers to an instrument whose spectral bands are constrained to the region of solar illumination, i.e.,
More informationStudy of the Influence of Thin Cirrus Clouds on Satellite Radiances Using Raman Lidar and GOES Data
Study of the Influence of Thin Cirrus Clouds on Satellite Radiances Using Raman Lidar and GOES Data D. N. Whiteman, D. O C. Starr, and G. Schwemmer National Aeronautics and Space Administration Goddard
More informationEstimation of evapotranspiration using satellite TOA radiances Jian Peng
Estimation of evapotranspiration using satellite TOA radiances Jian Peng Max Planck Institute for Meteorology Hamburg, Germany Satellite top of atmosphere radiances Slide: 2 / 31 Surface temperature/vegetation
More informationData assimilation of IASI radiances over land.
Data assimilation of IASI radiances over land. PhD supervised by Nadia Fourrié, Florence Rabier and Vincent Guidard. 18th International TOVS Study Conference 21-27 March 2012, Toulouse Contents 1. IASI
More informationField Emissivity Measurements during the ReSeDA Experiment
Field Emissivity Measurements during the ReSeDA Experiment C. Coll, V. Caselles, E. Rubio, E. Valor and F. Sospedra Department of Thermodynamics, Faculty of Physics, University of Valencia, C/ Dr. Moliner
More informationQuality assessment and validation of the MODIS global land surface temperature
INT. J. REMOTE SENSING, 10JANUARY, 2004, VOL. 25, NO. 1, 261 274 Quality assessment and validation of the MODIS global land surface temperature Z. WAN*, Y. ZHANG, Q. ZHANG ICESS, University of California,
More informationVIIRS SDR Cal/Val: S-NPP Update and JPSS-1 Preparations
VIIRS SDR Cal/Val: S-NPP Update and JPSS-1 Preparations VIIRS SDR Cal/Val Posters: Xi Shao Zhuo Wang Slawomir Blonski ESSIC/CICS, University of Maryland, College Park NOAA/NESDIS/STAR Affiliate Spectral
More informationVALIDATION OF THE AATSR LST PRODUCT AT THE VALENCIA TEST SITE: CAMPAIGNS
VALIDATION OF THE AATSR LST PRODUCT AT THE VALENCIA TEST SITE: 2002 2005 CAMPAIGNS César Coll, Vicente Caselles, Enric Valor, Raquel Nicl s, Juan M. S nchez and Joan M. Galve Department of Thermodynamics,
More informationCorrecting Microwave Precipitation Retrievals for near- Surface Evaporation
Correcting Microwave Precipitation Retrievals for near- Surface Evaporation The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation
More informationLAND SURFACE temperature (LST), as a key indicator
936 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 3, MARCH 2009 Developing Algorithm for Operational GOES-R Land Surface Temperature Product Yunyue Yu, Dan Tarpley, Jeffrey L. Privette,
More informationMETEOSAT SECOND GENERATION DATA FOR ASSESSMENT OF SURFACE MOISTURE STATUS
METEOSAT SECOND GENERATION DATA FOR ASSESSMENT OF SURFACE MOISTURE STATUS Simon Stisen (1), Inge Sandholt (1), Rasmus Fensholt (1) (1) Institute of Geography, University of Copenhagen, Oestervoldgade 10,
More informationLectures 7 and 8: 14, 16 Oct Sea Surface Temperature
Lectures 7 and 8: 14, 16 Oct 2008 Sea Surface Temperature References: Martin, S., 2004, An Introduction to Ocean Remote Sensing, Cambridge University Press, 454 pp. Chapter 7. Robinson, I. S., 2004, Measuring
More informationOSI SAF SST Products and Services
OSI SAF SST Products and Services Pierre Le Borgne Météo-France/DP/CMS (With G. Legendre, A. Marsouin, S. Péré, S. Philippe, H. Roquet) 2 Outline Satellite IR radiometric measurements From Brightness Temperatures
More informationInterannual variation of MODIS NDVI in Lake Taihu and its relation to climate in submerged macrophyte region
Yale-NUIST Center on Atmospheric Environment Interannual variation of MODIS NDVI in Lake Taihu and its relation to climate in submerged macrophyte region ZhangZhen 2015.07.10 1 Outline Introduction Data
More informationApplication of a Land Surface Temperature Validation Protocol to AATSR data. Dar ren Ghent1, Fr ank Göttsche2, Folke Olesen2 & John Remedios1
Application of a Land Surface Temperature Validation Protocol to AATSR data Dar ren Ghent1, Fr ank Göttsche, Folke Olesen & John Remedios1 1 E a r t h O b s e r v a t i o n S c i e n c e, D e p a r t m
More informationInter- Annual Land Surface Variation NAGS 9329
Annual Report on NASA Grant 1 Inter- Annual Land Surface Variation NAGS 9329 PI Stephen D. Prince Co-I Yongkang Xue April 2001 Introduction This first period of operations has concentrated on establishing
More informationUndergraduate Research Final Report: Estimation of suspended sediments using MODIS 250 m bands in Mayagüez Bay, Puerto Rico
Undergraduate Research Final Report: Estimation of suspended sediments using MODIS 250 m bands in Mayagüez Bay, Puerto Rico Abstract: José F. Martínez Colón Undergraduate Research 2007 802-03-4097 Advisor:
More informationGMES: calibration of remote sensing datasets
GMES: calibration of remote sensing datasets Jeremy Morley Dept. Geomatic Engineering jmorley@ge.ucl.ac.uk December 2006 Outline Role of calibration & validation in remote sensing Types of calibration
More informationGIFTS SOUNDING RETRIEVAL ALGORITHM DEVELOPMENT
P2.32 GIFTS SOUNDING RETRIEVAL ALGORITHM DEVELOPMENT Jun Li, Fengying Sun, Suzanne Seemann, Elisabeth Weisz, and Hung-Lung Huang Cooperative Institute for Meteorological Satellite Studies (CIMSS) University
More informationThermal Infrared (TIR) Remote Sensing: Challenges in Hot Spot Detection
Thermal Infrared (TIR) Remote Sensing: Challenges in Hot Spot Detection ASF Seminar Series Anupma Prakash Day : Tuesday Date : March 9, 2004 Time : 2.00 pm to 2.30 pm Place : GI Auditorium Geophysical
More informationEstimation of Wavelet Based Spatially Enhanced Evapotranspiration Using Energy Balance Approach
Estimation of Wavelet Based Spatially Enhanced Evapotranspiration Using Energy Balance Approach Dr.Gowri 1 Dr.Thirumalaivasan 2 1 Associate Professor, Jerusalem College of Engineering, Department of Civil
More informationEstimation and Validation of Land Surface Temperatures from Chinese Second-Generation Polar-Orbit FY-3A VIRR Data
Remote Sens. 2015, 7, 3250-3273; doi:10.3390/rs70303250 Article OPEN ACCESS remote sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Estimation and Validation of Land Surface Temperatures from
More informationTHERMAL MEASUREMENTS IN THE FRAMEWORK OF SPARC
THERMAL MEASUREMENTS IN THE FRAMEWORK OF SPARC J.A. Sobrino (1), M. Romaguera (1), G. Sòria (1), M. M. Zaragoza (1), M. Gómez (1), J. Cuenca (1), Y. Julien (1), J.C. Jiménez-Muñoz (1), Z. Su (2), L. Jia
More informationF O U N D A T I O N A L C O U R S E
F O U N D A T I O N A L C O U R S E December 6, 2018 Satellite Foundational Course for JPSS (SatFC-J) F O U N D A T I O N A L C O U R S E Introduction to Microwave Remote Sensing (with a focus on passive
More informationDeforestation and Degradation in Central and Southern Africa. Project Title: "Deforestation and Degradation in Central and Southern Africa"
Deforestation and Degradation in Central and Southern Africa Project Title: "Deforestation and Degradation in Central and Southern Africa" Description: An integrated land degradation and deforestation
More informationREVISION OF THE STATEMENT OF GUIDANCE FOR GLOBAL NUMERICAL WEATHER PREDICTION. (Submitted by Dr. J. Eyre)
WORLD METEOROLOGICAL ORGANIZATION Distr.: RESTRICTED CBS/OPAG-IOS (ODRRGOS-5)/Doc.5, Add.5 (11.VI.2002) COMMISSION FOR BASIC SYSTEMS OPEN PROGRAMME AREA GROUP ON INTEGRATED OBSERVING SYSTEMS ITEM: 4 EXPERT
More informationIn-Orbit Vicarious Calibration for Ocean Color and Aerosol Products
In-Orbit Vicarious Calibration for Ocean Color and Aerosol Products Menghua Wang NOAA National Environmental Satellite, Data, and Information Service Office of Research and Applications E/RA3, Room 12,
More informationHyperspectral Observations of Land Surfaces: Temperature & Emissivity
Hyperspectral Observations of Land Surfaces: Temperature & Emissivity Isabel F. Trigo Contributions from: Frank Göttsche, Filipe Aires, Maxime Paul Outline Land Surface Temperature Products & Requirements
More informationOn the Satellite Determination of Multilayered Multiphase Cloud Properties. Science Systems and Applications, Inc., Hampton, Virginia 2
JP1.10 On the Satellite Determination of Multilayered Multiphase Cloud Properties Fu-Lung Chang 1 *, Patrick Minnis 2, Sunny Sun-Mack 1, Louis Nguyen 1, Yan Chen 2 1 Science Systems and Applications, Inc.,
More informationHyperspectral Data as a Tool for Mineral Exploration
1 Hyperspectral Data as a Tool for Mineral Exploration Nahid Kavoosi, PhD candidate of remote sensing kavoosyn@yahoo.com Nahid Kavoosi Abstract In Geology a lot of minerals and rocks have characteristic
More informationComparison between Land Surface Temperature Retrieval Using Classification Based Emissivity and NDVI Based Emissivity
Comparison between Land Surface Temperature Retrieval Using Classification Based Emissivity and NDVI Based Emissivity Isabel C. Perez Hoyos NOAA Crest, City College of New York, CUNY, 160 Convent Avenue,
More informationStudying snow cover in European Russia with the use of remote sensing methods
40 Remote Sensing and GIS for Hydrology and Water Resources (IAHS Publ. 368, 2015) (Proceedings RSHS14 and ICGRHWE14, Guangzhou, China, August 2014). Studying snow cover in European Russia with the use
More informationLearning Objectives. Thermal Remote Sensing. Thermal = Emitted Infrared
November 2014 lava flow on Kilauea (USGS Volcano Observatory) (http://hvo.wr.usgs.gov) Landsat-based thermal change of Nisyros Island (volcanic) Thermal Remote Sensing Distinguishing materials on the ground
More informationComparison of NASA AIRS and MODIS Land Surface Temperature and Infrared Emissivity Measurements from the EOS AQUA platform
Comparison of NASA AIRS and MODIS Land Surface Temperature and Infrared Emissivity Measurements from the EOS AQUA platform Robert Knuteson, Hank Revercomb, Dave Tobin University of Wisconsin-Madison 16
More informationCORRELATION BETWEEN URBAN HEAT ISLAND EFFECT AND THE THERMAL INERTIA USING ASTER DATA IN BEIJING, CHINA
CORRELATION BETWEEN URBAN HEAT ISLAND EFFECT AND THE THERMAL INERTIA USING ASTER DATA IN BEIJING, CHINA Yurong CHEN a, *, Mingyi DU a, Rentao DONG b a School of Geomatics and Urban Information, Beijing
More informationTHE LAND-SAF SURFACE ALBEDO AND DOWNWELLING SHORTWAVE RADIATION FLUX PRODUCTS
THE LAND-SAF SURFACE ALBEDO AND DOWNWELLING SHORTWAVE RADIATION FLUX PRODUCTS Bernhard Geiger, Dulce Lajas, Laurent Franchistéguy, Dominique Carrer, Jean-Louis Roujean, Siham Lanjeri, and Catherine Meurey
More informationCOMPARISON OF NOAA/AVHRR AND LANDSAT/TM DATA IN TERMS OF RESEARCH APPLICATIONS ON THERMAL CONDITIONS IN URBAN AREAS
COMPARISON OF NOAA/AVHRR AND LANDSAT/TM DATA IN TERMS OF RESEARCH APPLICATIONS ON THERMAL CONDITIONS IN URBAN AREAS Monika J. Hajto 1 *, Jakub P. Walawender 2,3 and Piotr Struzik 2 1 Department of Air
More informationLunar Surface Material Composition Mapping
Introduction: Lunar Surface Material Composition Mapping Japan, India, China, and the United States have recently sent spacecraft orbiters to study the lunar surface. The main focus of these missions has
More informationPLEASE SCROLL DOWN FOR ARTICLE
This article was downloaded by:[university of Maryland] On: 13 October 2007 Access Details: [subscription number 731842062] Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered
More informationSOIL MOISTURE MAPPING THE SOUTHERN U.S. WITH THE TRMM MICROWAVE IMAGER: PATHFINDER STUDY
SOIL MOISTURE MAPPING THE SOUTHERN U.S. WITH THE TRMM MICROWAVE IMAGER: PATHFINDER STUDY Thomas J. Jackson * USDA Agricultural Research Service, Beltsville, Maryland Rajat Bindlish SSAI, Lanham, Maryland
More informationESM 186 Environmental Remote Sensing and ESM 186 Lab Syllabus Winter 2012
ESM 186 Environmental Remote Sensing and ESM 186 Lab Syllabus Winter 2012 Instructor: Susan Ustin (slustin@ucdavis.edu) Phone: 752-0621 Office: 233 Veihmeyer Hall and 115A, the Barn Office Hours: Tuesday
More informationGEOSC/METEO 597K Kevin Bowley Kaitlin Walsh
GEOSC/METEO 597K Kevin Bowley Kaitlin Walsh Timeline of Satellites ERS-1 (1991-2000) NSCAT (1996) Envisat (2002) RADARSAT (2007) Seasat (1978) TOPEX/Poseidon (1992-2005) QuikSCAT (1999) Jason-2 (2008)
More informationA NOVEL CONFIDENCE METRIC APPROACH FOR A LANDSAT LAND SURFACE TEMPERATURE PRODUCT INTRODUCTION
A NOVEL CONFIDENCE METRIC APPROACH FOR A LANDSAT LAND SURFACE TEMPERATURE PRODUCT Monica J. Cook and Dr. John R. Schott Rochester Institute of Technology Chester F. Carlson Center for Imaging Science Digital
More informationMeasuring and Analyzing of Thermal Infrared Emission Directionality over crop canopies with an airborne wide-angle thermal IR camera.
Measuring and Analyzing of Thermal Infrared Emission Directionality over crop canopies with an airborne wide-angle thermal IR camera. X. F. Gu 1, F. Jacob 1, J. F. Hanocq 1, T. Yu 1,2, Q. H. Liu 2, L.
More informationP3.24 EVALUATION OF MODERATE-RESOLUTION IMAGING SPECTRORADIOMETER (MODIS) SHORTWAVE INFRARED BANDS FOR OPTIMUM NIGHTTIME FOG DETECTION
P3.24 EVALUATION OF MODERATE-RESOLUTION IMAGING SPECTRORADIOMETER (MODIS) SHORTWAVE INFRARED BANDS FOR OPTIMUM NIGHTTIME FOG DETECTION 1. INTRODUCTION Gary P. Ellrod * NOAA/NESDIS/ORA Camp Springs, MD
More informationMonitoring Sea Surface temperature change at the Caribbean Sea, using AVHRR images. Y. Santiago Pérez, and R. Mendez Yulfo
Monitoring Sea Surface temperature change at the Caribbean Sea, using AVHRR images. Y. Santiago Pérez, and R. Mendez Yulfo Department of Geology, University of Puerto Rico Mayagüez Campus, P.O. Box 9017,
More informationExtending the use of surface-sensitive microwave channels in the ECMWF system
Extending the use of surface-sensitive microwave channels in the ECMWF system Enza Di Tomaso and Niels Bormann European Centre for Medium-range Weather Forecasts Shinfield Park, Reading, RG2 9AX, United
More informationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 52, NO. 9, SEPTEMBER
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 52, NO. 9, SEPTEMBER 2014 5587 Land-Surface Emissivity Retrieval in MSG SEVIRI TIR Channels Using MODIS Data Leonardo F. Peres, Renata Libonati,
More informationComparison of near-infrared and thermal infrared cloud phase detections
Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111,, doi:10.1029/2006jd007140, 2006 Comparison of near-infrared and thermal infrared cloud phase detections Petr Chylek, 1 S. Robinson,
More informationAnalysis of Pathfinder SST algorithm for global and regional conditions
Analysis of Pathfinder SST algorithm for global and regional conditions AJOY KUMAR,PETER MINNETT,GUILLERMO PODEST A ROBERT EVANS and KATHERINE KILPATRICK Meteorology and Physical Oceanography Division,
More informationTracking On-orbit Radiometric Accuracy and Stability of Suomi NPP VIIRS using Extended Low Latitude SNOs
Tracking On-orbit Radiometric Accuracy and Stability of Suomi NPP VIIRS using Extended Low Latitude SNOs Sirish Uprety a Changyong Cao b Slawomir Blonski c Xi Shao c Frank Padula d a CIRA, Colorado State
More informationImproving the CALIPSO VFM product with Aqua MODIS measurements
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln NASA Publications National Aeronautics and Space Administration 2010 Improving the CALIPSO VFM product with Aqua MODIS measurements
More information